Objective To systematically assess postoperative changes in physical activity (PA) and their influence on clinical outcomes among elderly patients after transcatheter aortic valve replacement (TAVR), providing an evidence-based framework for risk stratification and the design of personalized cardiac rehabilitation programs. Methods A systematic search was conducted in CNKI, Wanfang, SinoMed, PubMed, Web of Science, and the Cochrane Library for relevant literature published from April 16, 2002 to January 1, 2026. Eligible studies included patients with a mean age of ≥65 years who underwent TAVR, with assessments of PA both pre- and postoperatively, and reported clinical outcomes stratified by the trajectory of PA change. Two reviewers independently performed study selection, data extraction, and quality appraisal using the Newcastle-Ottawa Scale (NOS) for cohort studies and the Cochrane Risk-of-Bias tool for randomized controlled trials. ResultsFour studies, all assessed as high-quality (three cohort studies with an NOS score of 8 and one randomized controlled trial with "some concerns" for risk of bias) encompassing 1 278 patients were included. The mean age was (82.2±7.3) years, and 52.0% were female. The results demonstrated a strong association between postoperative PA trajectories and clinical outcomes. Patients with persistently low or declining PA exhibited significantly higher risks of all-cause mortality, composite cardiovascular events, and rehospitalization compared to those whose PA improved or remained stable. A notable "protective effect of functional improvement" was observed: patients with low baseline PA who achieved significant postoperative improvement had prognosis comparable to those with normal baseline PA. Multivariable analyses identified advanced age, female sex, comorbidities such as chronic obstructive pulmonary disease, and cognitive impairment as independent predictors of impaired postoperative PA recovery. Conclusion Dynamic postoperative PA trajectories are a key predictor of clinical outcomes in the elderly TAVR population. This allows for risk stratification to identify a "low-benefit" high-risk cohort, for whom targeted, individualized cardiac rehabilitation interventions are crucial to optimize long-term survival and enhance quality of life.
ObjectiveTo explore the role of preoperative evaluation indicators for decision-making on treatment modalities in papillary thyroid microcarcinoma (PTMC) with intermediate- and high-risk. MethodThe recent pertinent literatures on studies of risk factors influencing PTMC were collected and reviewed. ResultsThe surgical treatment was advocated for the PTMC with intermediate- and high-risk. However, the intraoperative surgical resection range and the postoperative prognosis of patients were debated. The malignancy of cell puncture pathology was a key factor in determining the surgical protocol. The patients with less than 45 years old at surgery, male, higher body mass index, higher serum thyrotropin level, and multifocal and isthmic tumors, and nodule internal hypoecho, calcification, unclear boundary, and irregular morphology by ultrasound, as well as mutations in BRAFV600E and telomerase reverse transcriptase gene were the risk factors for preoperative evaluation of PTMC with intermediate- and high-risk. ConclusionsAccording to a comprehensive understanding of preoperative risk factors for PTMC with intermediate- and high-risk, it is convenient to conduct an accurate preoperative evaluation and fully grasp the patients’ conditions. Clinicians should formulate individualized surgical treatment plans for patients based on preoperative assessment and their own clinical experiences.
ObjectiveTo summarize the recent advances and clinical applications of molecular testing in thyroid cancer, discussing its significance in the era of precision medicine and future perspectives. MethodsA systematic review of relevant domestic and international literature was conducted to identify key molecular events closely associated with the development, progression, and prognosis of thyroid cancer, and to evaluate their clinical utility. ResultsMolecular testing provides critical auxiliary diagnostic information for thyroid nodules with indeterminate fine-needle aspiration results. Furthermore, for diagnosed differentiated thyroid cancer, molecular markers serve as important tools for precise risk stratification, guiding surgical extent, radioactive iodine therapy decisions, and targeted drug applications. ConclusionMolecular testing has become a cornerstone tool in advancing thyroid cancer management toward precision medicine, future efforts should focus on exploring novel molecular markers and optimizing clinical practice guidelines.
ObjectiveTo explore the application value of machine learning models in predicting postoperative survival of patients with thoracic squamous esophageal cancer. MethodsThe clinical data of 369 patients with thoracic esophageal squamous carcinoma who underwent radical esophageal cancer surgery at the Department of Thoracic Surgery of Northern Jiangsu People's Hospital from January 2014 to September 2015 were retrospectively analyzed. There were 279 (75.6%) males and 90 (24.4%) females aged 41-78 years. The patients were randomly divided into a training set (259 patients) and a test set (110 patients) with a ratio of 7 : 3. Variable screening was performed by selecting the best subset of features. Six machine learning models were constructed on this basis and validated in an independent test set. The performance of the models' predictions was evaluated by area under the curve (AUC), accuracy and logarithmic loss, and the fit of the models was reflected by calibration curves. The best model was selected as the final model. Risk stratification was performed using X-tile, and survival analysis was performed using the Kaplan-Meier method with log-rank test. ResultsThe 5-year postoperative survival rate of the patients was 67.5%. All clinicopathological characteristics of patients between the two groups in the training and test sets were not statistically different (P>0.05). A total of seven variables, including hypertension, history of smoking, history of alcohol consumption, degree of tissue differentiation, pN stage, vascular invasion and nerve invasion, were included for modelling. The AUC values for each model in the independent test set were: decision tree (AUC=0.796), support vector machine (AUC=0.829), random forest (AUC=0.831), logistic regression (AUC=0.838), gradient boosting machine (AUC=0.846), and XGBoost (AUC=0.853). The XGBoost model was finally selected as the best model, and risk stratification was performed on the training and test sets. Patients in the training and test sets were divided into a low risk group, an intermediate risk group and a high risk group, respectively. In both data sets, the differences in surgical prognosis among three groups were statistically significant (P<0.001). ConclusionMachine learning models have high value in predicting postoperative prognosis of thoracic squamous esophageal cancer. The XGBoost model outperforms common machine learning methods in predicting 5-year survival of patients with thoracic squamous esophageal cancer, and it has high utility and reliability.
The American Heart Association (AHA) and the American College of Cardiology (ACC), in collaboration with multiple professional organizations, jointly released the "Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults" in August 2025. Based on the latest evidence-based medical findings from February 2015 to January 2025, the guideline proposes an individualized treatment strategy grounded in total cardiovascular disease risk stratification, incorporates the novel PREVENT risk assessment model, lowers the medication initiation threshold and control targets for high-risk populations, and provides specific management recommendations for special populations. This article provides an interpretation of these updates and conducts a comparative analysis with the current status of hypertension prevention and treatment in China as well as Chinese guidelines, aiming to offer reference for hypertension control practices in China.
ObjectiveTo develop and validate a machine learning model based on preoperative clinical characteristics, laboratory indices, and radiological features for the non-invasive prediction of spread through air spaces (STAS) in patients with early-stage lung adenocarcinoma. Methods Preoperative data from patients with early-stage lung adenocarcinoma who underwent surgical resection at Northern Jiangsu People's Hospital between January 2020 and August 2025 were retrospectively collected. The data included clinical characteristics, laboratory indices, and radiological features. Patients were divided into a STAS-positive and a STAS-negative group based on postoperative pathological findings. The dataset was randomly split into a training set and a testing set at a 7 : 3 ratio. Feature variables were selected using the maximum relevance and minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression. Five machine learning models were constructed: logistic regression (LR), random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The shapley additive explanations (SHAP) method was employed to interpret the optimal prediction model. Results A total of 377 patients were included, comprising 177 (46.9%) males and 200 females (53.1%), with a mean age of (63.31±9.73) years. There were 261 patients in the training set and 116 patients in the testing set. In the training set, statistically significant differences were observed between the STAS-positive group (n=130) and STAS-negative group (n=131) across multiple features, including age, sex, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), clinical T stage, and maximum solid component diameter (P<0.05). A final set of 10 feature variables was selected by combining mRMR and LASSO regression, and five machine learning models (LR, RF, SVM, LightGBM, XGBoost) were developed. The XGBoost model demonstrated superior predictive performance in both the training and testing sets, achieving AUCs of 0.947 [95%CI (0.920, 0.975)] and 0.943 [95%CI (0.894, 0.993)], respectively, and achieved the optimal level in the testing set. DCA indicated that the XGBoost model provided a high net clinical benefit across a wide range of threshold probabilities. SHAP analysis revealed that the vessel convergence sign, clinical T stage, age, consolidation-to-tumor ratio (CTR), and MLR were the features with the highest contributions to STAS prediction. Conclusion The XGBoost model effectively predicts preoperative STAS status in early-stage lung adenocarcinoma, exhibiting excellent discriminative performance and good clinical interpretability. Key predictors such as the vessel convergence sign, clinical T stage, age and CTR provide a crucial reference for preoperative risk assessment and the individualized selection of surgical strategies, ultimately benefiting patients.
ObjectiveTo understand the current status of low-density lipoprotein cholesterol (LDL-C) control in patients after coronary artery bypass grafting (CABG). MethodsClinical data of patients who underwent isolated CABG in Beijing Anzhen Hospital in 2023 were collected. All patients returned to our hospital approximately one year after surgery (10-13 months) for a lipid level recheck. We analyzed their LDL-C attainment status and influencing factors. Patients were categorized into two groups based on whether their LDL-C met the target: a LDL-C attainment group and a LDL-C non-attainment group. ResultsThis study included 1456 patients who underwent CABG, including 320 females and 1136 males, with an average age of (61.41±9.12) years. One year post-surgery, 234 patients achieved the LDL-C target, with an attainment rate of 16.07%. The proportion of patients in the LDL-C attainment group who were ultra-high risk (77.35% vs. 92.06%, P＜0.001), female (16.24% vs. 23.08%, P=0.021), and those with comorbid hypertension (55.98% vs. 63.18%, P=0.038) was significantly lower than those in the LDL-C non-attainment group. Additionally, the baseline body mass index (BMI) [(25.37±3.24) kg/m2 vs. (26.03±3.56) kg/m2, P=0.017], total cholesterol levels [(3.30±0.84) mmol/L vs. (4.01±1.03) mmol/L, P＜0.001], LDL-C [(1.62±0.63) mmol/L vs. (2.25±0.85) mmol/L, P＜0.001], and high-density lipoprotein cholesterol [(0.98±0.26) mmol/L vs. (1.02±0.24) mmol/L, P=0.049] upon admission in the attainment group were all lower than those in the non-attainment group. Moreover, the lipid-lowering drug usage rate in the attainment group (100.00% vs. 96.24%, P=0.003) and the proportion using two types of drugs together (25.21% vs. 10.72%, P＜0.001) were both higher than those in the non-attainment group, while the statin monotherapy rate was lower than that in the non-attainment group (74.79% vs. 85.19%, P＜0.001). Logistic regression analysis showed that baseline BMI (OR=0.928, P=0.012) and baseline LDL-C levels (OR=0.207, P<0.001), patient cardiovascular risk stratification (OR=0.155, P<0.001) and lipid-lowering drug treatment regimen (OR=3.758, P<0.001) are significant factors affecting the LDL-C control status. ConclusionThe LDL-C compliance rate of patients undergoing CABG is at a relatively low level 1 year after surgery. Patients with very high risk of atherosclerotic cardiovascular disease, high baseline LDL-C levels, and overweight or obesity should be strengthened lipid management. For these patients, the intensity of lipid-lowering drug use or combination medication should be increased upon discharge.